In a typical Hidden Markov Model (HMM) based recognition system, an input pattern such as a handwritten word, is represented as a time-ordered sequence of observations, usually in the form of feature vectors. Observation probabilities derived from these feature vectors are presented to a network of HMM states. Various methods can then be applied to find either the optimal state sequence or the most likely model for the given observation sequence, thus providing the recognition result.
Stochastic pattern recognizers, such as Hidden Markov Model (HMM) based recognizers are typically point oriented in that the observations are often localized in nature. For example, observations for sampled on-line handwriting might include point positions, interpoint vector orientation such as stroke tangents and curvature. All of these features can, in principle, be measured by looking at one, two, or three handwriting data points, although in practice, the smoothing filters used to reduce noise, require additional local data points to support each measurement. For each new sample data point, taken in chronological sequence, the HMM hypotheses scores are discretely integrated and propagated through the HMM network.
Alternatively, stochastic pattern recognizers can be segment oriented. A segmental feature is a measurement of some characteristic of a contiguous collection of sample points as for example by a sliding window measurement, or as further described below by applying segmental features selected through point based features. In this design, a script is first segmented into letters or subcharacter primitives according to defined boundary conditions such as pen-ups and cusps, as further described below. After the segmentation step a single observation feature vector is computed for each segment.
Point oriented methods avoid actually generating all possible segmentations by simultaneous scoring of all hypotheses and immediate pruning of poorly scored partial hypotheses. Consequently, all possible segmentations and identifications of the input pattern are considered in an efficient manner. However, since point oriented methods only utilize local observation measurements, the system does not consider the shape of the sample, which can only be observed from larger scale measurements.
One method for obtaining shape information in a point oriented system is to extract features from a window of fixed or variable size around each sample point. This method, however, does not adapt to the varying characteristics of pattern shapes, sizes, and segmentation boundaries.
Systems using segment oriented methods obtain greater accuracy by integrating observations over larger regions, but operate less efficiently than point oriented methods. Unlike point oriented systems where each point is analyzed independently, many alternate segments in a segment oriented system can be modelled for a given set of sample points. To generate all segmentations and compute all possible hypothesis scores is an intractable task, thus, impractical.